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Cli d lli idClimate modelling: A guide to 
investment decisions
Vicky Pope
Met Office Hadley Centre
The impact of a global temperature rise 
of 4 ºCof 4  C
Change in temperature from pre‐industrial climate
1    2    3    4    5    6    7    8    9    10   11  12  13  14  15  16
Melting
ice
Methane 
release
M
Ocean
Reduced 
crops
Forest
Increased 
drought
More 
heatwaves
Ocean
Acidification
Rainforest
loss
Forest
fire Stronger 
tropical storms
Current City population • 3‐10 million • 10‐20 million© Crown copyright   Met Office
The benefits of integrated services:
for policy and long term planning
Bridging the Gaps and Bridging the Scales
GlobalGlobal
Weather/Climate
Model: 25 -100km
Regional
Weather/Climate Regional Impacts
M d l
Regional Impacts
Models: Hydrology,
model: 25 - 12km Model:Vegetation,
Topography
Local
Local Decision-
Making: Land
use, Water use,
Local
downscaling
model:
4 1km use, a e use,
Adaptive
Responses
4 - 1km
© Crown copyright   Met Office
Future Nile flow: 
A Climate Change Risk Management Programmeg g g
• Impact on water resources 
by 2050s
• Possible adaptation
• Regional climate models 
d Nil f tiand Nile forecasting 
system
• Risk based approachRisk based approach
• Rainfall and river runoff –
wide range of possible 
outcomes
Image NASA © Crown copyright   Met Office
Changes in rain and snowfall in the high 
mountains of South Asiamountains of South Asia
Himachal Pradesh East Nepal &
W E
Summer precipitation (Indian monsoon)Winter precipitation (Western disturbances)
NW
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Karakoram Hindu Kush Jammu‐Kashmir
Himachal Pradesh, 
Uttarakhand & West Nepal
East Nepal &
Bhutan
ion mm/day
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
nge in precipitat
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Chanday
Karakoram
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
n snowfall mm/d
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Change in
Time ‐ Met Office model   ‐ MPI model
Probability forecasts of coastal flood risk:
Balanced approach to decision making
Central estimate Upper limit of
pp g
Central estimate 
from UKCP models
Upper limit of 
"high++“ model
Change in 
extreme sea 
level of 50 year y
storm
© Crown copyright   Met Office
Energy project 
HeadlineHeadline 
results
© Crown copyright   Met Office
© Crown copyright   Met Office
The benefits of integrated services:
for planning and civil contingencies
Seamless prediction
Supporting decision making
Now
ours
Days
week
onth
onal
cadal
mate
mate
nce 
ry
N
H
D
1‐w
1‐mo
Seas
Dec
Clim
Past clim
Confiden
bounda
Analysis of past weather 
observations to manageobservations to manage 
climate risks
Eg. Agriculture: this informs 
crop choice and planting date 
to optimise yields and di i i d
Monthly to decadal 
predictions  informs 
probability of drought, cold, 
heat.to optimise yields and 
minimise crop failure risk.
Predicting routine and 
hazardous weather conditions 
and disseminating tailored 
and timely warnings. 
P bli
heat.
Contingency planners, 
national and international 
humanitarian response, 
government and private 
Global and regional climate 
predictions.
Informs mitigation policy and 
adaptation choices Impacts
© Crown copyright   Met Office
Forecast lead‐time
Public, emergency response, 
international disaster risk 
reduction 
g p
infrastructure investment
adaptation choices. Impacts 
on water resurces, heat 
stress, crops, infrastructure.
Example of seasonal prediction of 
reservoir management  
Lake Volta dam Akosombo
g
Ghana
Lake Volta dam, Akosombo
• 1000MWatt facility: provides ~50% of 
Ghana’s electricity 
Lake 
Volta
• Rainfall has strong seasonal dependency: 
peak months June‐September
• Inflow prediction needed to assess likely 
requirement for oil‐fired generation
• …thus lake‐level is monitored closely!…thus lake level is monitored closely!
© Crown copyright   Met Office
Summary
• Useful risk based climate information forUseful risk based climate information for 
adaptation ‐ e.g. River Nile, Hamalayas
• Long range forecasts for decision tools MoreLong range forecasts for decision tools. More 
skilful in some regions and aggregated over 
regions such as large water catchments – Voltaregions such as large water catchments  Volta 
• ‘Seamless prediction’ addresses climate 
variability and changevariability and change
• Integrated approach provides environmental 
information for decision makinginformation for decision making
© Crown copyright   Met Office

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Climate Modeling – A Guide to Investment

  • 1. Cli d lli idClimate modelling: A guide to  investment decisions Vicky Pope Met Office Hadley Centre
  • 2. The impact of a global temperature rise  of 4 ºCof 4  C Change in temperature from pre‐industrial climate 1    2    3    4    5    6    7    8    9    10   11  12  13  14  15  16 Melting ice Methane  release M Ocean Reduced  crops Forest Increased  drought More  heatwaves Ocean Acidification Rainforest loss Forest fire Stronger  tropical storms Current City population • 3‐10 million • 10‐20 million© Crown copyright   Met Office
  • 4. Bridging the Gaps and Bridging the Scales GlobalGlobal Weather/Climate Model: 25 -100km Regional Weather/Climate Regional Impacts M d l Regional Impacts Models: Hydrology, model: 25 - 12km Model:Vegetation, Topography Local Local Decision- Making: Land use, Water use, Local downscaling model: 4 1km use, a e use, Adaptive Responses 4 - 1km © Crown copyright   Met Office
  • 5. Future Nile flow:  A Climate Change Risk Management Programmeg g g • Impact on water resources  by 2050s • Possible adaptation • Regional climate models  d Nil f tiand Nile forecasting  system • Risk based approachRisk based approach • Rainfall and river runoff – wide range of possible  outcomes Image NASA © Crown copyright   Met Office
  • 6. Changes in rain and snowfall in the high  mountains of South Asiamountains of South Asia Himachal Pradesh East Nepal & W E Summer precipitation (Indian monsoon)Winter precipitation (Western disturbances) NW JK 000 2050 2100 HP 1950 2000 2050 2100 -2 0 2 4 6 NB 1950 2000 2050 2100 -2 0 2 4 6 JK 000 2050 2100 HP 1950 2000 2050 2100 -10 0 10 20 30 40 NB 1950 2000 2050 2100 -10 0 10 20 30 40 JK 000 2050 2100 HP 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 NB 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 JK 000 2050 2100 HP 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 NB 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 Karakoram Hindu Kush Jammu‐Kashmir Himachal Pradesh,  Uttarakhand & West Nepal East Nepal & Bhutan ion mm/day JK 000 2050 2100 HP 1950 2000 2050 2100 -2 0 2 4 6 NB 1950 2000 2050 2100 -2 0 2 4 6 JK 000 2050 2100 HP 1950 2000 2050 2100 -10 0 10 20 30 40 NB 1950 2000 2050 2100 -10 0 10 20 30 40 JK 000 2050 2100 HP 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 NB 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 JK 000 2050 2100 HP 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 NB 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 nge in precipitat JK 000 2050 2100 HP 1950 2000 2050 2100 -2 0 2 4 6 NB 1950 2000 2050 2100 -2 0 2 4 6 JK 000 2050 2100 HP 1950 2000 2050 2100 -10 0 10 20 30 40 NB 1950 2000 2050 2100 -10 0 10 20 30 40 JK 000 2050 2100 HP 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 NB 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 JK 000 2050 2100 HP 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 NB 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 Chanday Karakoram JK 000 2050 2100 HP 1950 2000 2050 2100 -2 0 2 4 6 NB 1950 2000 2050 2100 -2 0 2 4 6 JK 000 2050 2100 HP 1950 2000 2050 2100 -10 0 10 20 30 40 NB 1950 2000 2050 2100 -10 0 10 20 30 40 JK 000 2050 2100 HP 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 NB 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 JK 000 2050 2100 HP 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 NB 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 n snowfall mm/d JK 000 2050 2100 HP 1950 2000 2050 2100 -2 0 2 4 6 NB 1950 2000 2050 2100 -2 0 2 4 6 JK 000 2050 2100 HP 1950 2000 2050 2100 -10 0 10 20 30 40 NB 1950 2000 2050 2100 -10 0 10 20 30 40 JK 000 2050 2100 HP 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 NB 1950 2000 2050 2100 -0.5 0.0 0.5 1.0 1.5 JK 000 2050 2100 HP 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 NB 1950 2000 2050 2100 -1.5 -1.0 -0.5 0.0 0.5 1.0 Change in Time ‐ Met Office model   ‐ MPI model
  • 7. Probability forecasts of coastal flood risk: Balanced approach to decision making Central estimate Upper limit of pp g Central estimate  from UKCP models Upper limit of  "high++“ model Change in  extreme sea  level of 50 year y storm © Crown copyright   Met Office
  • 10. Seamless prediction Supporting decision making Now ours Days week onth onal cadal mate mate nce  ry N H D 1‐w 1‐mo Seas Dec Clim Past clim Confiden bounda Analysis of past weather  observations to manageobservations to manage  climate risks Eg. Agriculture: this informs  crop choice and planting date  to optimise yields and di i i d Monthly to decadal  predictions  informs  probability of drought, cold,  heat.to optimise yields and  minimise crop failure risk. Predicting routine and  hazardous weather conditions  and disseminating tailored  and timely warnings.  P bli heat. Contingency planners,  national and international  humanitarian response,  government and private  Global and regional climate  predictions. Informs mitigation policy and  adaptation choices Impacts © Crown copyright   Met Office Forecast lead‐time Public, emergency response,  international disaster risk  reduction  g p infrastructure investment adaptation choices. Impacts  on water resurces, heat  stress, crops, infrastructure.
  • 11. Example of seasonal prediction of  reservoir management   Lake Volta dam Akosombo g Ghana Lake Volta dam, Akosombo • 1000MWatt facility: provides ~50% of  Ghana’s electricity  Lake  Volta • Rainfall has strong seasonal dependency:  peak months June‐September • Inflow prediction needed to assess likely  requirement for oil‐fired generation • …thus lake‐level is monitored closely!…thus lake level is monitored closely! © Crown copyright   Met Office
  • 12. Summary • Useful risk based climate information forUseful risk based climate information for  adaptation ‐ e.g. River Nile, Hamalayas • Long range forecasts for decision tools MoreLong range forecasts for decision tools. More  skilful in some regions and aggregated over  regions such as large water catchments – Voltaregions such as large water catchments  Volta  • ‘Seamless prediction’ addresses climate  variability and changevariability and change • Integrated approach provides environmental  information for decision makinginformation for decision making © Crown copyright   Met Office